Coagulation potential and the integrated omics of extracellular vesicles from COVID-19 positive patient plasma

Extracellular vesicles (EVs) participate in cell-to-cell communication and contribute toward homeostasis under physiological conditions. But EVs can also contribute toward a wide array of pathophysiology like cancer, sepsis, sickle cell disease, and thrombotic disorders. COVID-19 infected patients are at an increased risk of aberrant coagulation, consistent with elevated circulating levels of ultra-high molecular weight VWF multimers, D-dimer and procoagulant EVs. The role of EVs in COVID-19 related hemostasis may depend on cells of origin, vesicular cargo and size, however this is not well defined. We hypothesized that the procoagulant potential of EV isolates from COVID-19 (+) patient plasmas could be defined by thrombin generation assays. Here we isolated small EVs (SEVs) and large EVs (LEVs) from hospitalized COVID-19 (+) patient (n = 21) and healthy donor (n = 20) plasmas. EVs were characterized by flow cytometry, Transmission electron microscopy, nanoparticle tracking analysis, plasma thrombin generation and a multi-omics approach to define coagulation potential. These data were consistent with differences in EV metabolite, lipid, and protein content when compared to healthy donor plasma isolated SEVs and LEVs. Taken together, the effect of EVs on plasma procoagulant potential as defined by thrombin generation and supported by multi-omics is enhanced in COVID-19. Further, we observe that this effect is driven both by EV size and phosphatidyl serine.

Quantification of isolated extracellular vesicles. Intact SEVs and LEVs were resuspended in PBS and used in our thrombin generation assay. Protein quantification of the SEVs and LEVs was performed using the Micro BCA Protein Assay Reagent Kit based on the manufacturer's instructions (Thermo Scientific, Waltham, MA, USA). Briefly, assay reagents were mixed and incubated at room temperature for 1 min followed by addition of samples and incubation for 30 min at room temperature and read at 562 nm using synergy HTX reader (BioTek instruments, Winooski, Vermont, USA). To check the consistency, protein quantification of isolated SEVs and LEVs was also done with NanoDrop One spectrophotometer (Thermo Scientific, Waltham, MA, USA). Sample absorbance at 280 nm was used for protein quantification, using PBS as a blank. Both the techniques provided similar values, so NanoDrop was used for further measurements.
Residual platelet count measurements. Residual platelets in platelet poor plasma from healthy donors, COVID-19 (+) patient and pooled plasma for EV depleted plasma, SEV and LEV preparations were quantified using ABX Pentra 60 C + Hematology analyzer (HORIBA Instruments Inc., Kyoto, Japan). Briefly, 60 µL of plasma samples after different centrifugation steps were used for the analyses. 200 µg/mL of SEVs and LEVs were added to EV depleted plasma and used for the measurements. Further, residual platelets were evaluated by flow cytometry. Briefly, healthy plasma, 800×g supernatant, 3200×g supernatant and 10,000×g supernatants were analyzed by flow cytometry using CD41 as marker for platelets (Fig. S1B).
Dynamic light scattering. The hydrodynamic size of isolated SEVs and LEVs were determined by the dynamic light scattering (DLS) principle using a Zetasizer (Nano ZS, Malvern Instruments, Malvern, UK).
Transmission electron microscopy. Isolated SEVs and LEVs from COVID-19 (+) patient and healthy donor samples were evaluated by transmission electron microscopy (TEM) to visualize their size and morphology. Briefly, 10 μL of SEV and LEV suspensions were added to Formvar/carbon coated 200 mesh copper grids and allowed to dry for 2 min at room temperature and the excess suspension was wiped off with Whatman filter paper. Samples were then washed briefly with nanopure water and stained with 2% aqueous uranyl acetate (10 μL) for 2 min. The stained SEVs and LEVs were observed using a transmission electron microscope (Hitachi HT7800, Tokyo (HQ) Japan) at an acceleration voltage of 60 kV.
Antibodies. The following list of antibodies were used in the flowcytometry analysis and TG assays: Alexa Flow cytometric characterization of SEVs and LEVs. Flow cytometry analysis was performed to classify the cellular origin of purified SEVs and LEVs as platelet, red blood cell, or endothelial cell derived, based on their surface markers. SEVs and LEVs were stained for 45 min at ambient temperature in the dark with a combination of Alexa Fluor 647 conjugated anti-CD63, Alexa Fluor 488 conjugated anti-CD81, BV421 conjugated anti-CD41, PE dazzle 594 conjugated anti-CD235a, BV605 conjugated anti-CD31, and PE conjugated anti-CD62P antibodies. Antibody solutions were centrifuged at 17,000×g for 10 min at 4 °C before incubation to avoid artefacts caused by antibody aggregates 38 . After incubation, SEVs and LEVs were resuspended in 500 μL particle free PBS and analyzed. Negative controls included buffer alone and unstained SEV and LEV samples. Single staining for specific antibodies was performed to determine the background. Each markers expression was represented as a histogram and normalized to mode. Forward scatter (FSC) and side scatter (SSC) were www.nature.com/scientificreports/ set to gate the SEV and LEV population. Calibration was done with flow cytometry sub-micron particle size reference kit (0.1, 0.2, 0.5 µm; cat # F13839, ThermoFisher scientific) according to manufacturer instruction. The SEV gate was set above the 200 nm particle distribution which includes the 100 nm and 200 nm beads clouds and gate for LEVs was set above the 500 nm bead population to include the distribution of all other beads, as shown in Fig. S2A. A detergent lysis control was performed to confirm the purity of the intact EVs signals. Both SEVs and LEVs were treated with 0.25% TritonX-100 during the staining process (CD63) (Fig. S3A). Control experiments for flowcytometric characterization was performed with buffer control, isotype control, and single staining of EVs characterization markers (CD63 and CD81) (Fig. S3B).
To evaluate the true expression of cell specific EVs, a blocking (cold inhibition) experiment was performed. Excess specific unlabeled antibodies (20X) were used to block the epitopes on the EV surface for 30 min incubation at room temperature and then incubated with fluorescently labeled antibody for 30 min at room temperature and analyzed as compared to the single stained samples (Fig. S4). All samples were evaluated using a BD FACSAria Flow cytometer (Becton Dickenson, Vancouver BC, Canada) and acquired data was analyzed using FlowJo 10.8.0 software (Becton Dickenson, Vancouver BC, Canada).
Thrombin generation. TG was performed based on the assay originally developed by H. C. Hemker 39 with modifications. Briefly, concentration (50, 100, 200 µg protein/mL) dependent SEV and LEV fractions were incubated with 70 µL of EV depleted human platelet poor pooled plasma (EDP). Samples were supplemented with buffer (150 mM NaCl, 20 mM HEPES at pH 7.5) and mixed with thrombin specific substrate, Z-Gly-Gly-Arg-AMC (Bachem, Bubendorf, Switzerland) at 3.08 mM final concentration. AMC fluorophore was added for the calibration measurement. The reaction was started by adding an activator solution comprised of a final concentration of 2 pM tissue factor (Diagnostica Stago, Parsippany, NJ, USA), 0.7 µg/mL of tissue plasminogen activator (tPA) (Sigma-Aldrich, St. Louis, MO, USA) and 16 mM CaCl 2 . Calculation and analysis of the TG curve was performed as previously described 40,41 . TM and annexin V blocking experiments were performed using SEV and LEV isolates (100 µg protein/mL) incubated with 10 µg/mL of anti-human CD141 (TM) antibody and 0.1, 0.5, 2.5 µg/mL annexin V (Biolegend, San Diego, California, USA) at ambient temperature for 1 h. The TG assay was carried out as described above.
Tissue factor activity. Tissue factor (TF) activity was measured using a human tissue factor activity assay kit (ab108906, Abcam, Cambridge, United Kingdom) with slight modifications to the manufactures protocol. 200 µg protein/mL SEV or LEV was incubated with the assay mixture overnight at 4 °C followed by a 30 min incubation at 37 °C. After addition of FXa substrate readings were recorded at 405 nm every 2 min for 1 h at 37 °C using synergy HTX reader (BioTek instruments, Winooski, Vermont, USA). The assay modifications were specific to the duration of mixture incubation being extended to overnight for the purposes of this study.

Metabolomics and lipidomics.
High-throughput metabolomics analysis was performed on SEVs and LEVs isolated from healthy donor and COVID-19 (+) patient plasmas. Samples were thawed on ice and metabolites or lipids extracted by adding −20 °C methanol:acetonitrile:water (5:3:2 v/v/v) or methanol to each tube, respectively-at a 1:9 sample:extraction solution ratio (v/v), followed by resting for 20 min at −20 °C and centrifugation at 4 °C. All extracts were analyzed twice (20 µL injection each) by ultra-high-performance liquid chromatography using a UHPLC Vanquish coupled with a Q Exactive mass spectrometer (both from ThermoFisher Scientific Inc., San Jose, CA, USA) using both negative and positive polarity modes. For each method, the UHPLC utilized an Acquity HSS column at a flow rate increasing from 0.3 to 0.4 mL/min or 0.325 to 0.4 mL/min for 17 or 15 min for lipidomics, or at a flow rate of 450 µL/min on a Kinetex C18 column (150 × 2.1 mm, 1.7 μm, Phenomenex, Torrance, CA, USA) using 5-min gradients in positive and negative ion polarity modes for metabolomics, respectively, as described previously 42,43 . Proteomics. Protein pellets from metabolomics/lipidomics samples were digested in an S-Trap filter (Protifi, Huntington, NY), following the manufacturer's procedure. Briefly, ~ 50 μg of protein were first mixed with 5% SDS. Samples were reduced with 10 mM dithiothreitol at 55 °C for 30 min, cooled to room temperature, and then alkylated with 25 mM iodoacetamide in the dark for 30 min. Phosphoric acid was then added to a final concentration of 1.2% followed by 6 volumes of binding buffer (90% methanol; 100 mM triethylammonium bicarbonate (TEAB); pH 7.1). After gentle mixing, the protein solution was loaded onto an S-Trap filter, centrifuged (2000×g; 1 min), and the flow-through collected and reloaded onto the filter. This step was repeated three times, and then the filter was washed with 200 μL of binding buffer 3 times. Finally, 1 μg of sequencing- www.nature.com/scientificreports/ grade trypsin and 150 μL of digestion buffer (50 mM TEAB) were added onto the filter and digested at 47 °C for 1 h. To elute peptides, three step-wise buffers were applied, with 200 μL of each with one more repeat; these included 50 mM TEAB, 0.2% formic acid in water, and 50% acetonitrile and 0.2% formic acid in water. The peptide solutions were pooled, lyophilized, and resuspended in 0.1% formic acid. Samples (200 ng each) were loaded onto individual Evotips for desalting and then washed with 20 μL 0.1% formic acid followed by adding 100 μL of storage solvent (0.1% formic acid) to keep the Evotips wet until analysis. The Evosep One system was coupled to the timsTOF Pro mass spectrometer (Bruker Daltonics, Bremen, Germany). Data were collected over an m/z range of 100-1700 for MS and MS/MS on the timsTOF Pro instrument using an accumulation and ramp time of 100 ms. Post processing was performed with PEAKS studio (Version X+, Bioinformatics Solutions Inc., Waterloo, ON). Pathway analyses were performed with DAVID software and Ingenuity Pathway Analysis.
Statistical analysis. All statistical analyses and graphing of data were performed using GraphPad Prism software (version 9.2.0). Comparisons across groups were analyzed with a one-way-ANOVA and Tukey's multiple comparison test. Lipidomic data analysis was performed using Maven (1.4.20-dev-772), and quality controls were maintained as described 42 . LipidSearch (4.2.27) and Compound Discoverer (3.1.0.305) in tandem performed untargeted data analysis. Heatmaps and correlation data were generated by MetaboAnalyst (5.0) 44 . Graphs were produced using GraphPad Prism (9.2.0).

General and clinical characteristics of study subjects
COVID-19 (+) patient demographics and comorbidities are described in Table 1. Clinical characteristics, including severity scoring, clinical laboratory values, pre-hospital, and in-hospital drug therapies are described in Table 2.
Characterization of extracellular vesicles. First, we determined the particle sizes of SEVs and LEVs that were isolated from healthy donor and COVID-19 (+) patient plasmas. The average healthy donor SEV size was 136.06 ± 33.66 nm compared to 122.54 ± 56.46 nm for SEV isolated from COVID-19 (+) patients. The  Fig. 1A,B respectively. Figure 1C represents the mean particle size of healthy donor and COVID-19 (+) patient derived SEVs and LEVs. TEM analysis representative images are shown in Fig. 1D reveal visual differences consistent with data obtained from particles sizing. Western blotting analysis (Fig. 1E) displayed EV specific tetraspanin markers (CD63 and CD81) expression of healthy donor SEVs/LEVs and COVID-19 (+) patient SEVs/LEVs and EDP as negative control.

COVID-19 (+) patient
Extracellular vesicle thrombomodulin, phosphatidylserine, and tissue factor. TM is an endothelial cell surface glycoprotein that forms a complex with thrombin and subsequently activates protein C to inactivate FVIIIa and FVa 29 . Therefore, cell surface-bound TM modulates the anticoagulant effect of thrombin. Soluble TM is increased in COVID-19 infection and is a potential marker of endothelial injury 46 . To explore the potential role of TM in COVID-19 (+) patient plasma we isolated SEVs and LEVs and performed blocking experiments to inhibit TM in both SEV and LEV concentrates. Neither healthy donor nor COVID-19 (+) patient plasma isolated SEVs demonstrated changes in TG after anti-TM incubation as shown in Fig. 4A-C. Conversely, both healthy donor and COVID-19 (+) patient plasma isolated LEVs increased TG peak velocities following anti-TM incubation and this effect was significantly (p = 0.0269) greater in COVID-19 (+) patient LEVs compared to all other EV types as shown in Fig. 4D-F. We next performed EV surface blocking experiments with annexin V at three concentrations (0.1, 0.5, 2.5 µg/ mL) to specifically quench the effects of PS mediated TG. Preincubation of healthy donor and COVID-19 (+) patient SEVs and LEVs with annexin V inhibited TG in a dose dependent manner (Fig. 5A,B). Importantly COVID-19 patient LEVs showed increased TG compared to EDP and healthy LEVs at 0.1 and 0.5 µg/mL annexin V concentration (Peak height: p < 0.0001; Peak velocity: 0.1 µg/mL, p < 0.0001). Healthy donor and COVID-19 (+) patient plasma isolated SEVs did not show a significant difference in TG, peak height, and peak velocity at any of the concentrations of annexin V (0.1, 0.5, 2.5 µg/mL) compared to EDP (Fig. 5C,D). In contrast, there were significant differences observed between COVID-19 (+) patient plasma isolated LEVs and EDP for peak height (0.1 µg/mL and 0.5 µg/mL: p < 0.0001) and peak velocity (0.1 µg/mL: p < 0.0001 and 0.5 µg/mL: p = 0.0129) of TG curves at 0.1 and 0.5 µg/mL annexin V concentrations (Fig. 5C,D). At 0.1 µg/mL annexin V both peak height and velocity increased significantly in COVID-19 (+) plasma LEVs compared to healthy LEVs (Fig. 5C,D; p < 0.0001). At 0.1 µg/mL annexin V preincubation, COVID-19 (+) patient LEVs showed TG, but healthy donor LEVs did not (Fig. 5C,D). This suggests that surface accessible PS on COVID-19 (+) patient plasma isolated LEVs is one of the major contributors to TG in the patient samples studied here.
To account for the contribution of TF, we evaluated TF in our healthy donor and COVID-19 (+) patient plasma isolated SEVs and LEVs. Our analysis suggests that TF activity was not different (Healthy SEVs vs. COVID-19 SEVs p = 0.8366; Healthy LEVs vs. COVID-19 LEVs p = 0.3839) between COVID-19 (+) patients and healthy donor plasma isolated SEVs or LEVs (Fig. S6). This observation may be specific to COVID-19 as it contrasts with studies that show TF-activity microparticles across a range of disease states. A TF activity assay for both the SEVs and LEVs did not demonstrate significant difference between COVID-19 (+) patients and healthy donors (Fig. S6).

Omics characterization of SEV and LEV from COVID-19 (+) patients and healthy donors
To further understand potential differences in EV populations we performed a multi-omics analysis of SEVs and LEVs from COVID-19 (+) patients and healthy donors, including metabolomics (Fig. 6), lipidomics (Fig. 7), and proteomics (Fig. 8). Results are reported in tabulated form in Supplementary Table 1, for SEVs and LEVs.
Given the differences in the lipid species amenable to detection from basic metabolomics analyses, we thus performed an untargeted, more comprehensive analyses of the lipidomes of these samples (Fig. 7). Volcano plots (color-coded by lipid classes), bar plots (broken down by lipid classes), and heat maps of the top 50 most significant lipids by unpaired two-tailed T-test are shown for LEVs and SEVs in Fig. 7A-C,D-F, respectively. Both LEV and SEV from COVID-19 (+) patients were characterized by higher levels of lysophophatidylethanolamines (LPEs), lyosphosphatidylcholines (LPCs), and phosphatidylcholines (PCs). SEV showed also higher levels of unsaturated, long-chain free fatty acids-expanding on metabolomics data-and lower levels of cholesterylesters (ChE)- (Fig. 7E).
Of all omics analyses, the most striking differences between LEV and SEV from COVID-19 (+) patients and healthy controls were observed in the proteome (Fig. 8). Supplementary Fig. S7 shows the results of an unsupervised hierarchical clustering of LEV and SEV samples (Fig. 8A,B) and a network view (Fig. S7)  Other proteins enriched in these groups-like spectrins SPTA1 and SPTB-are suggestive of components of erythrocytic origin. Specific to SEVs, a decrease in multiple keratins and serin-protease inhibitors (SERPINC1, A6, but not G1) was observed in COVID-19 (+) patients (Fig. 8C,D).

Omics correlates to thrombin generation.
To tie omics analyses to functional readouts of the procoagulant activity of LEVs and SEVs derived from COVID-19 (+) patients, we performed (Spearman) correlation analyses (Fig. 9A,B, respectively-for the 200 μg dose). Results noted that, while TG parameters (rate, peak height, peak velocity) were strongly positively correlated among each other (r ~ 1 in both LEVs and SEVs), protein components-followed by lipids-showed the strongest correlation to the functional measurements. Of note, several significant (merged) omics correlates to TG rate were identified for LEVs (Fig. 9A), but not SEVs (Fig. 9B)-consistent with a higher procoagulant activity of the former. Several phosphatidylcholines (PCs), triacylglycerols, coenzyme Q9 and ceramides (Hex1Cer) ranked amongst the strongest negatively correlated parameters to TG rate in LEV (Fig. 9A). Similarly, multiple amino acids (aspartate, methionine, phenylalanine, tyrosine) and proteins (TRIM61) negatively correlated with TG rate in LEVs (Fig. 9A), while only proteins (especially ANXA2, CLU, DSC1, PROC, DSG1) and lipids (sphingosine) showed significant positive correlations www.nature.com/scientificreports/ to TG rate in LEVs (Fig. 9A). ANXA2 levels were positively associated with TG rate in both LEVs and SEVs (Fig. 9A,B).

Discussion
In this study we compared the thrombogenic potential of SEVs and LEVs isolated from COVID-19 (+) patient and healthy donor plasmas. Our results suggest enrichment for EV specific markers CD63, CD81, and CD62P and procoagulant PS in COVID-19 (+) patient plasma EVs compared to healthy donors. We report for the first time the impact of COVID-19 (+) patient plasma LEVs ability to increase TG and identify metabolic, lipid, and protein correlates consistent with coagulopathy. EVs play a key role in the pathogenesis of various disease conditions, including COVID-19 infection 18,19 . In this study, SEVs and LEVs were purified from COVID-19 (+) patient and healthy donor plasmas using a polymer-based reagent and sequential centrifugation, respectively. Flow cytometry confirmed the enrichment of EV specific markers CD63, CD81 and CD62P in COVID-19 (+) patient LEVs compared to healthy donors and COVID-19 (+) patient SEVs.
Initially EVs were characterized by their procoagulant activity and termed "platelet dust" 47 . The exposure of anionic phospholipids-especially PS-is an important factor that contributes to the procoagulant activity of EVs [48][49][50][51] . PS exposure on intact platelets and platelet-derived microparticles has been widely studied 48   www.nature.com/scientificreports/ Different levels of PS + platelet EVs are reported in COVID-19 (+) patients due to the severity of disease 35,56 . To date we know of one study that has implemented flow cytometric evaluation of COVID-19 (+) patient whole blood and reported on elevated levels of CD41 and CD31 specific EVs 30 . Experiments performed to block SEV and LEV surface PS with annexin V confirm an increased PS-mediated TG peak height and velocity caused by LEVs isolated from our COVID-19 (+) patient plasmas. This suggests that PS is an important contributor to LEV-mediated aberrant coagulation in COVID-19. Conversely, TM plays a critical role in anticoagulation and acts to mediate the catalysis of protein C (positively correlated with TG rate in LEVs) activation following thrombin binding. Normally TM is closely associated with the endothelium, but it is reported to be increased in the circulating plasma of COVID-19 (+) patients, presumably due to diffuse endothelial injury consistent with the disease. Here we observed that the TG was greater after TM blocking in COVID-19 (+) patient plasma isolated LEVs compared to LEVs from healthy donors, indicating a loss of TM from the endothelium.
To understand assay measurements of thrombin mediated procoagulant activity of SEVs and LEVs from COVID-19 (+) patients and healthy donors, we also performed a combined metabolomics, lipidomics, and proteomics analysis on the same samples. Results confirmed an enrichment for platelet-derived proteins in the SEVs and LEVs in both groups, with a fraction of EVs likely of erythrocytic origin. Further proteomic analysis revealed VWF, F13A1 (factor XIII A subunit), F13B (factor XIII B subunit), FGA (alpha subunit of fibrinogen), FGB (beta component of fibrinogen), GP1BA (glycoprotein Ib-alpha)-were all procoagulant proteins and/or of platelet origin enriched in both LEVs and SEVs from COVID-19 (+) patients. Of note, positive correlation between the levels of annexin 2 (ANXA2) and TG rate were observed for both LEVs and SEVs, consistent with the role of this peripheral membrane binding protein in lipid segregation and membrane budding 57 . These results are in keeping with and expand on previous omics studies of sera 58 and red blood cells from COVID-19 (+) patients, the latter showing that oxidative damage to the erythrocyte membrane likely fueled vesiculation of blood cell-derived protein components and complex lipids as a function of disease severity 59 . Omics analyses also confirmed a significant alteration of the lipidome of LEVs from COVID-19 (+) patients, which were www.nature.com/scientificreports/ found to be enriched with several phospholipid classes (PCs, LPCs, LPEs, with PEs ranking amongst the top correlates to TG parameters with other neutral lipids, e.g., triacylglycerols). Of note, lipidomics analyses of LEVs showed a significant negative correlation between lipid levels (especially triacylglycerols and ceramides) and TG parameters, while higher PS exposure (but not levels) in LEVs was positively associated with it. This observation suggests that a combination of lipidome composition and compartmentalization contributes to the procoagulant effect of LEVs from COVID-19 (+) patients and that PS is more externalized to the LEV surface. Unexpectedly, lower levels of oxylipins were observed in EVs from COVID-19 (+) patients -metabolites that are enriched in mammalian red blood cells and negatively correlate with erythrocyte propensity to undergo splenic sequestration and extravascular hemolysis 60 . Though speculative at this stage, this finding-combined with proteomics results and flow-cytometry data-is suggestive of a lesser erythrocytic component to the EV concentration in the circulating blood of COVID-19 (+) patients compared to healthy donors. Expanding on previous hypotheses that associate the role of blood cell-derived vesicles to the alterations of the circulating lipidome of COVID-19 (+) patients as a function of disease severity, as gleaned by IL-6 levels and pre-existing conditions (e.g., obesity) 11 . Besides EVs serving as a marker for vascular damage they play an important role in the pathogenesis of blood clot formation and are reported to increase TG in several disease states 61 . In this study we observed a significant increase in TG parameters induced by addition of COVID-19 (+) patient plasma isolated LEVs compared to COVID-19 (+) patient SEVs and compared to healthy donor plasma isolated SEVs and LEVs. TG associated with the COVID-19 (+) patients LEV fraction of plasma accumulated EVs in this study supports an observation of increased TG in COVID-19 (+) patient versus acutely ill patient plasmas upon hospital presentation and admission 62 . This study does have several limitations as follows: all samples from confirmed COVID-19 (+) patients were obtained within 1-3 days of hospital admission which may alter the cargo of both SEVs and LEVs and temporal samples over the course of hospital stay to assess progressive disease were not obtained. Additionally, the data is derived from a small group size (n ≤ 21) for both COVID-19 (+) and healthy donor plasma samples and the COVID-19 (+) patient population demonstrates a wide range of pre-existing health conditions and pre-hospital medications that may have impacted SEV and LEV composition. In this study, we were limited to small volume (4 mL) blood collections and as a result polymer based SEV isolation was our primary option. Further, the   www.nature.com/scientificreports/ In conclusion, this study is the first to report the role of COVID-19 (+) patient plasma isolated SEVs and LEVs on induction of TG. COVID-19 (+) patient plasma derived LEVs were determined to be of platelet, RBC, and endothelial cell origins in plasmas of COVID-19 (+) patients. A notable procoagulant factor on the cumulative LEV population was PS; and capping of this phospholipid effectively reduced LEV induced TG. The presentation of functional TM on the surface of COVID-19 (+) patient plasma isolated EVs suggests an added risk of microvascular coagulation due to TM loss from the endothelium. Other novel findings identify VWF, F13A1, F13B, FGA, FGB, GP1BA consistent with platelet enriched components of LEVs. Further, the omics signature of COVID-19 (+) patient plasma derived LEVs suggests a utility for a simple TG assay to define the early thrombogenic risk in COVID-19 and other acute and chronic diseases with a known component of hemostasis risk.

Data availability
The datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.